English
讲座编号：jz-yjsb-2022-y017
讲座问题：Activation discovery with FDR control Application to fMRI data
主 讲 人：王兆军 教授 南开大学
讲座时间：2022年5月11日（星期三）下午14:00
讲座所在：腾讯聚会，聚会ID:518 222 217
加入工具：数学与统计学院全体西席及研究生
主理单位：数学与统计学院、研究生院
主讲人简介：
王兆军，南开大学统计与数据科学学院教授、博士生导师、执行院长和党总支书记，统计研究院院长，中国现场统计研究会副理事长，中国工业统计教学研究会副会长，天津数据科学与手艺学会理事长，天津市学位委员会数学与统计学科评议组召集人，曾获国务院政府特殊津贴，天下百篇优博指导西席，教育部天下高校自然科学奖二等奖及天津市自然科学奖一等奖。王兆军教授的主要研究偏向包括统计历程控制(SPC)、非(半)参数回归、降维、高维数据剖析、变点等，已在Journal of the American Statistical Association、Annals of Statistics、Biometrika、Statistica Sinica等专业顶级期刊上揭晓高质量学术论文110余篇，先后主持国家自然科学基金重点项目、面上项目、教育部博士点基金项目等10余项，现担当Statistical Theory and Related Fields、《统计信息论坛》、《数学希望》等杂志编委和《数理统计与治理》等杂志副主编。
主讲内容：
Data arriving in “streams” from a large number of sources is ubiquitous, a portion of which usually incurs structural changes during the time-course of data acquisition. For example, in fMRI analysis, some brain regions become active associated with task-related stimuli or even in resting-states. Such a region corresponds to an activated data stream. We are aiming to measure the uncertainty of discovering data streams in activation via the tool of the false discovery rate (FDR). Borrowing ideas from recent developments of the FDR control methodologies, we propose a simple yet effective method to achieve this purpose meanwhile taking unknown asynchronous change patterns and spatial dependence into consideration. Its validity on controlling the FDR is justified by asymptotic analysis. Numerical experiments indicate that the proposed method is both accurate and powerful. It is also applied in a real fMRI data analysis. A R package SLIP is developed to implement the proposed method.
